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mindspore.ops.one_hot

mindspore.ops.one_hot(indices, depth, on_value=1, off_value=0, axis=- 1)

Computes a one-hot tensor.

The locations represented by indices in indices take value on_value, while all other locations take value off_value.

Note

If the input indices has rank N, the output will have rank N+1. The new axis is created at dimension axis. On Ascend, if on_value is int64 dtype, indices must be int64 dtype, and the value for on_value and off_value can only be 1 and 0.

Parameters

• indices (Tensor) – A tensor of indices. Tensor of shape $(X_0,\dots ,X_n)$. Data type must be int32 or int64.

• depth (int) – A scalar defining the depth of the one-hot dimension.

• on_value (Union[Tensor, int, float], optional) – A value to fill in output when indices[j] = i. Data type must be int32, int64, float16 or float32. Default: 1 .

• off_value (Union[Tensor, int, float], optional) – A value to fill in output when indices[j] != i. Has the same data type as on_value. Default: 0 .

• axis (int, optional) – Position to insert the value. e.g. If shape of self is $(N,C)$, and axis is -1, the output shape will be $(N,C,depth)$, If axis is 0, the output shape will be $(depth,N,C)$. Default: -1 .

Returns

Tensor, one-hot tensor. Tensor of shape $(X_0,\dots ,X_{axis},\text{depth},X_{axis+1},\dots ,X_n)$, and it has the same data type as on_value.

Raises

• TypeError – If axis or depth is not an int.

• TypeError – If dtype of indices is not int32 or int64.

• TypeError – If dtype of on_value is not int32, int64, float16 or float32.

• TypeError – If indices, on_value or off_value is not a Tensor.

• ValueError – If axis is not in range [-1, ndim].

• ValueError – If depth is less than 0.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> indices = Tensor(np.array([0, 1, 2]), mindspore.int32)
>>> depth, on_value, off_value = 3, Tensor(1.0, mindspore.float32), Tensor(0.0, mindspore.float32)
>>> output = ops.one_hot(indices, depth, on_value, off_value, axis=-1)
>>> print(output)
[[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]]